Off-the-shelf allogeneic polyclonal CD38KO/CD38-CAR γδT cells for the treatment of T cell malignancies
Bibliographic record
Abstract
Abstract Relapsed and refractory T cell malignancies are associated with poor clinical outcomes. Autologous sources of αβT cells have been employed for chimeric antigen receptor (CAR) therapies to eliminate the potential for graft-vs-host disease (GvHD). However, the application of CAR-T therapy for T-ALL has been hindered by an inability to obtain sufficient healthy αβT cells from patients combined with fratricide due to concurrent antigen expression on normal T cells. Here, we genetically engineered polyclonal γδT cells, which do not cause GvHD, as an allogeneic source for cancer immunotherapy targeting the pancancer antigen CD38. Utilizing a novel expansion protocol in combination with CRISPR/AAV gene editing, we developed CD38KO/CD38-CAR polyclonal γδT cells that target T-ALL. Our editing strategy enabled site-directed, on-target insertion of the CD38-CAR transgene into the CD38 locus, with no evidence of significant random CAR DNA integration (as commonly seen with lentiviral CAR transduction) or chromatin abnormalities resulting from CRISPR editing. This enhanced targeting effectively mitigated fratricide through simultaneous CD38 disruption and CAR expression. We demonstrated the efficacy of the CD38KO/CD38-CAR γδT cells in vitro across multiple patient-derived T-ALL samples collected at baseline and relapse. In vivo, a single injection of CD38KO/CD38-CAR γδT cells without exogenous cytokine support resulted in potent anti-leukemic efficacy. Fratricide-resistant CD38KO/CD38-CAR polyclonal γδT cells thus represent a promising off-the-shelf therapeutic platform for T cell malignancies and other CD38-expressing cancers. Key Points Hybrid pan-γδTCR antibody/mbIL21-41BBL feeder expansion yields high-purity, polyclonal γδT cells suitable for CRISPR/AAV editing. On-target CD38-CAR knock-in with simultaneous CD38 knockout prevents fratricide and enables potent T-ALL killing in vitro and in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".